The present invention relates to semiconductor devices and fabrication methods thereof, and more particularly, to methods of controlling semiconductor device manufacturing processes and control systems for such processes.
Semiconductor devices are manufactured through various processes. For instance, semiconductor devices are manufactured by performing sequential processes including crystal growth of a semiconductive material, manufacturing a wafer from the semiconductive crystal, etching, doping, ion-implantation, packaging, and final testing. However, these sequential processes may be performed at different apparatuses using different control methods. A control system to precisely control a process condition is often necessary or desirable to maintain appropriate statuses of the individual processes. In most of the manufacturing processes, process conditions can be controlled appropriately by controlling an execution time of the process (hereinafter referred to as the “process time”). For example, a time controlled process can be a rapid thermal process, a chemical mechanical polishing (CMP) process, an overlay process, a physical deposition process, a chemical deposition process, or a spin coating process.
In the case of a CMP process, the thickness of a material removed by the CMP process varies depending on a process time. A conventional CMP process is carried out by being divided into a sample CMP process and a main CMP process. The sample CMP process determines a removal rate (Å/sec) from a blanket wafer where patterns are not formed. A process time for an actual wafer to be polished to remove material according to the determined removal rate (i.e., a polishing time) is calculated empirically and, afterwards, the sample CMP process is performed.
If a thickness deviation by the sample CMP process is within an allowable range, the main CMP process is performed. The process time can be controlled via a manual feedback by continuously checking thicknesses of lots to which the main CMP process is applied. For instance, if a removal thickness of the lot after the main CMP process is larger than an intended thickness, the process time is shortened, and if less than intended, the process time is lengthened. Herein, the process time is empirical, and the CMP process can be applied to one product without difficulty.
In the case of multiple types of products (e.g., in a system LSI manufacturing line) the densities of patterns for each product are different from each other, and, thus, the process times are different from each other. Therefore, process times are empirically collected and made into a separate table for each product (hereinafter referred to as “process table”) and, when a specific product is subjected to a corresponding process, the process time stored in the process table is used.
However, the execution of the processes for the multiple types of products based on the empirical data may have a potential risk of an error, or may be highly sensitive to a manufacturer's mistake, which might bring out a poor process distribution. Also, in the case of a CMP process, consumable articles such as a polishing pad may not function properly as time elapses. As a result, a removal rate may be reduced, and this reduced removal rate may cause a poor or inconsistent process distribution. An advanced process control (APC) method that calculates a process time via an automatic input of the conventional empirical process condition has been implemented systematically to reduce a degree of process distribution. According to the APC method, a process time can be calculated automatically using a conversion factor (CF) for process times of products.
However, in contrast to those conventional or previously processed products, which can be manufactured with less limitation with the implementation of the APC method, manufacture of newly developed products may be limited because there is no established process table due to novelty of the products and a difficulty in the implementation of the APC method due to restricted use of the conversion factor.